Ensuring the security of stable, efficient and reliable energy supplies has intensified the interconnections between energy markets. Imbalances between supply and demand due to operational failures, congestion and other sources of risk faced by market connections can lead to a system that is vulnerable to the spread of risk and its spill-over. The main contribution of this paper is the development and estimation of a Bayesian Graphical Vector-AutoRegression and a Bayesian Graphical Structural Equation Modelling with external regressors - BG-VARX and BG-SEMX, respectively - enhancing the proper analysis of market connections. The Italian electricity market has been chosen because it is a clear example of a zonal market where risk can spread over connected zones. We estimate, for the first time, within-day and across-day zonal market interconnections with a multivariate time series of hourly prices, actual and forecast power demand and forecast wind generation over the period 2014–2019 and evaluate the dynamics and persistence of zonal market connections, examining the spread of risk in the zones of the Italian electricity market. Our findings provide an improved, accurate explanation of risk contagion, identifying the zones that are most influential in terms of hub centrality (major transmitters) and authority centrality (major recipients), respectively, for intra-day and inter-day risk propagation in the Italian electricity market. In addition, the policy implications in terms of market-monitoring are discussed.